From Sinhala to Dhivehi: Cross-Lingual Transfer Learning for Low-Resource Speech Recognition
Dhivehi, the national language of the Maldives, is currently under-resourced for automatic speech recognition (ASR) and other NLP tasks. This study investigates whether cross-lingual transfer learning from Sinhala, a linguistically related, relatively well-resourced Insular Indo-Aryan language, can improve Dhivehi ASR. We conduct seventeen experiments across five transfer learning paradigms: Dhivehi-only baselines, sequential fine-tuning, multilingual fine-tuning, continual pre-training, and a control using Turkish as an unrelated language. The strongest system, continual pre-training on Sinhala followed by fine-tuning on Dhivehi with KenLM, achieves 12.89% WER and 2.70% CER, outperformin...
| Authors | Lukmal Ilyas, Nevidu Jayatilleke |
|---|---|
| Primary category | cs.CL |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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